import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import RMSprop
from keras.utils import np_utils
import numpy as np

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

history = model.fit(
    x_train, y_train,
    batch_size=128,
    epochs=20,
    verbose=1,
    validation_data=(x_test, y_test)
)
